Lead Data Intelligence Project Manager
Dyson
Unknown, دبي, الإمارات العربية المتحدة
Contract
Competitive
HybridManager
At Dyson, we’re driven by a relentless pursuit of innovation—pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dyson’s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.
You’ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.
As the Lead Data Intelligence Project Manager, you ensure that every project has the resources it needs to take off, you'll orchestrate data operations that power product development across Dyson’s global teams. Key responsibilities include:
Act as the primary liaison between our external project teams and our internal technical departments (Data Curation and MLOps). You will own the "data logistics" for the entire project lifecycle—from initial storage setup to final delivery of high-quality training sets.
You will be the strategic lead for our labelling workforce, managing the balance between our in-house contractors and 3rd party service providers to ensure we never hit a capacity bottleneck.
Overseeing data storage setup and maintenance to guarantee security, accessibility, and compliance.
Maintaining rigorous documentation and workflows that ensure project transparency and repeatability.
Supporting cross-functional communication between data teams, contractors, and engineering partners.
8+ years in Project or Program Management, with at least 2 years specifically in AI/ML Data Operations or Data Management. You’ve delivered complex projects on time and within scope—often in fast-changing settings.
Demonstrated success in managing 3rd party vendors or service providers, including contract negotiation, quality assurance, issue resolution, and ongoing relationship management.
Machine Learning Lifecycle Knowledge: Strong understanding of how data flows from collection → cleaning → labeling → training → deployment.
Tooling Familiarity: Experience with labeling platforms (Labelbox, CVAT, Label Studio) and project management tools (Jira, Asana, or Airtable).
Data Literacy: Basic understanding of data storage architectures (S3, SQL, Snowflake) and data privacy regulations (GDPR, CCPA).
Communication: Exceptional "translation" skills—the ability to explain technical MLOps constraints to business stakeholders and vice versa.
A solid understanding of modern data storage solutions (cloud, on-premises, hybrid), including principles of data security, privacy, access control, and regulatory compliance.
Able to translate complex technical requirements into clear actions for both technical and non-technical audiences. You’re adept at influencing and building consensus.
Confident interfacing with cross-functional teams—coordinating with data scientists, engineers, product managers, and external stakeholders to deliver smooth handovers and alignment.
Well-versed in project management tools and methodologies (e.g., Agile, Scrum, or Waterfall). Known for driving continuous improvement, clear documentation, and repeatable processes.
Thorough, highly organized, and relentless in troubleshooting issues and removing blockers before they impact delivery.
Proactive, accountable, and comfortable juggling competing priorities. You always keep end goals in focus and balance urgency with quality.
Bachelor’s degree in a relevant discipline (e.g., Computer Science, Data Engineering, Engineering, or Information Technology); higher degree or PMP/Prince2 certification is a plus.
Dyson is an equal opportunity employer. We know that great minds don’t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.